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Record W2012963814 · doi:10.1109/pedg.2012.6254021

Reliability comparison of power electronic converters used in grid-connected wind energy conversion system

2012· article· en· W2012963814 on OpenAlexaff
Md Arifujjaman, Liuchen Chang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsConvertersWind powerMean time between failuresBuck converterPower optimizerElectrical engineeringPower (physics)Reliability (semiconductor)Power electronicsRectifier (neural networks)EngineeringMaximum power point trackingTurbinePermanent magnet synchronous generatorElectronic engineeringComputer scienceInverterMagnetVoltagePhysicsFailure rateMechanical engineeringReliability engineering

Abstract

fetched live from OpenAlex

This work presents a reliability analysis of the power electronic converters for a grid-connected permanent magnet generator-based wind energy conversion system based on the semi conductor power losses. The power converters examined are: the intermediate boost converter (IBC), the intermediate buck-boost converter (IBBC), the back-to-back converter (BBC) and the matrix converter (MC). The aim is to determine which power electronic converter yields the highest mean time between failures (MTBF) and reliability in terms of power losses of the semiconductor devices with a predetermined wind speed. In view of this, a furled wind turbine model developed previously by the author is used to generate power for different wind speeds. Afterwards, a relation between the wind speed, power loss and MTBF is established to evaluate the reliability of the power electronic converters. The power loss model presented in this paper has taken into account the conduction and switching losses of the semiconductor devices within each converter. The analysis reveals that MTBF of an IBC is much higher compared to the other converters considered in this research. The investigation is extended to identify the least reliable component within the converters. It is shown that the inverter has the dominant effect on the system reliability for the converters. This research indicates that IBC with a simple rectifier is a much better option for grid-connected permanent magnet generator based wind energy conversion system.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.864
Threshold uncertainty score0.906

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.212
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations22
Published2012
Admission routes1
Has abstractyes

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